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							<div class="topic open">Fundamentals</div>
							
								<div class="subtopic attachment">The purpose of the IA and its historical evolution</div>
								
								<div class="subtopic">Cognitive abilities according to psychology</div>
								
								<div class="subtopic">The pattern of knowledge acquisition</div>
								
								<div class="subtopic selected attachment">The cognitive model</div>
								
								<div class="subtopic attachment">The intelligent agent model</div>
								
								<div class="subtopic">The role of heuristic</div>
							
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								<a>Artificial Intelligence I</a> &gt; <a>Fundamentals</a> &gt; <a>The cognitive model</a>
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							<div class="date open">Monday December 12th, 2011</div>
						
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								<div class="note_labels">cognitive model</div>
								A cognitive model is an approximation to animal cognitive processes (predominantly human) for the purposes of comprehension and prediction. Cognitive models can be developed within or without a cognitive architecture, though the two are not always easily distinguishable.
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								<div class="note_labels">behavior, soar, architecture</div>
								In contrast to cognitive architectures, cognitive models tend to be focused on a single cognitive phenomenon or process (e.g., list learning), how two or more processes interact (e.g., visual search and decision making), or to make behavioral predictions for a specific task or tool (e.g., how instituting a new software package will affect productivity). Cognitive architectures tend to be focused on the structural properties of the modeled system, and help constrain the development of cognitive models within the architecture. Likewise, model development helps to inform limitations and shortcomings of the architecture. Some of the most popular architectures for cognitive modeling include ACT-R and Soar.
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								<div class="note_labels">analytics, mathematical model</div>
								A computational model is a mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation. The system under study is often a complex nonlinear system for which simple, intuitive analytical solutions are not readily available. Rather than deriving a mathematical analytical solution to the problem, experimentation with the model is done by changing the parameters of the system in the computer, and studying the differences in the outcome of the experiments. Theories of operation of the model can be derived/deduced from these computational experiments. Examples of common computational models are weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, and neural network models.
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							<div class="date collapsed">Tuesday December 13th, 2011</div>
						
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